Institute of Information Systems
Organisational unit: Institute
- Junior professorship for Information Systems, in particular Data Analytics
- Professorship for Computer Engineering
- Professorship for Information Systems, in particular Artificial Intelligence and Explainability
- Professorship for Information Systems, in particular Data Science
- Professorship for Information Systems, in particular Digital Transformation and Information Management
- Professorship for Information Systems, in particular E-Business and Knowledge Management
- Professorship for Information Systems, in particular Machine Learning
- Professorship for Information Systems, in particular Network Science
Organisation profile
At the Institute of Information Systems (IIS) of the Leuphana University we work on innovative topics in Information Systems and Data Science. The main focus is on digital transformation, e-health, e-commerce, business analytics, sports and e-learning. Methodologically, we focus on the development and use of machine learning and artificial intelligence methods for the modeling and solution of data-driven decision problems. Additional quantitative (e.g. graphs, optimization) and qualitative (e.g. interviews, reference modeling) methods complement this spectrum. Cooperating with other universities, companies, and institutions of the civil society plays an important role in our work.
Main research areas
At the Institute of Information Systems there are currently 6 professors and numerous research assistants. Our main research areas include digital transformation, e-health, e-commerce, business analytics, sports, and e-learning. Details can be found on the websites of the work groups and in the central research database of Leuphana. The Institute of Information Systems organized a number of events and conferences at Leuphana (e.g. ITEE 2013, final round of the German National Computer Science Competition 2014, MKWI 2018).
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Gründungsplanung - aber wie?
Weihe, J., 1990, Berufliche Selbständigkeit als "verlockende" Karrierealternative: Begleitschrift zum Workshop vom 03.März 1990. Norbert, S., Weihe, H. J. & Fischer, R. (eds.). Schwarzenbek: Verlag Dr. Rüdiger Martienss, p. 1-37 37 p.Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Transfer
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Guest editorial: Special issue on sports analytics
Brefeld, U. & Zimmermann, A., 01.11.2017, In: Data Mining and Knowledge Discovery. 31, 6, p. 1577-1579 3 p.Research output: Journal contributions › Other (editorial matter etc.) › Research
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Hands in Focus: Sign Language Recognition Via Top-Down Attention
Sarhan, N., Wilms, C., Closius, V., Brefeld, U. & Frintrop, S., 08.10.2023, 2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings: Proceedings. Piscataway: IEEE Electromagnetic Compatibility Society, p. 2555-2559 5 p. (Proceedings - International Conference on Image Processing, ICIP).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Herausforderungen in der Wirtschaftsprüfung durch den Einsatz von Big Data-Technologien: Welche Impulse muss die Prüfungsforschung geben?
Velte, P. & Drews, P., 26.10.2018, In: Der Betrieb. 71, 43, p. 2581-2585 5 p.Research output: Journal contributions › Journal articles › Transfer
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Heterogeneity Matters: Predicting Self-Esteem in Online Interventions Based on Ecological Momentary Assessment Data
Bremer, V., Funk, B. & Riper, H., 13.01.2019, In: Depression Research and Treatment. 2019, 9 p., 3481624.Research output: Journal contributions › Journal articles › Research › peer-review
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Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining
Daduna, H., Krenzler, R., Ritter, R. & Stoyan, D., 03.2018, In: Performance Evaluation. 119, p. 5-26 22 p.Research output: Journal contributions › Journal articles › Research › peer-review
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How Big Does Big Data Need to Be?
Stange, M. & Funk, B., 06.2016, Enterprise Big Data Engineering, Analytics, and Management. Atzmueller, M., Oussena, S. & Roth-Berghofer, T. (eds.). Hershey: Business Science Reference, p. 1-12 12 p.Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review
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How does Enterprise Architecture support the Design and Realization of Data-Driven Business Models? An Empirical Study
Rashed, F. & Drews, P., 01.01.2021, Innovation Through Information Systems - Volume III: A Collection of Latest Research on Management Issues. Ahlemann, F., Schütte, R. & Stieglitz, S. (eds.). Cham: Springer Nature Switzerland AG, p. 662-677 16 p. (Lecture Notes in Information Systems and Organisation; vol. 48 LNISO).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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How Much Home Office is Ideal? A Multi-Perspective Algorithm
Colley, M., Jansen, P., Matthiesen, J., Hoberg, H., Reger, C. & Thiermann, I., 20.09.2023, Proceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work, CHIWORK 2023. Boll, S., Cox, A., Ludwig, T. & Cecchinato, M. E. (eds.). New York: Association for Computing Machinery, Inc, 12 p. 8. (ACM International Conference Proceeding Series).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis
Stange, M. & Funk, B., 29.05.2015, Proceedings of the Twenty-Third European Conference on Information Systems. AIS eLibrary, 13 p.Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review